index int64 0 20.3k | text stringlengths 0 1.3M | year stringdate 1987-01-01 00:00:00 2024-01-01 00:00:00 | No stringlengths 1 4 |
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4,300 | On Causal Discovery with Cyclic Additive Noise Models Joris M. Mooij Radboud University Nijmegen, The Netherlands j.mooij@cs.ru.nl Dominik Janzing Max Planck Institute for Intelligent Systems T¨ubingen, Germany dominik.janzing@tuebingen.mpg.de Tom Heskes Radboud University Nijmegen, The Netherla... | 2011 | 239 |
4,301 | Prediction strategies without loss Michael Kapralov Stanford University Stanford, CA kapralov@stanford.edu Rina Panigrahy Microsoft Research Silicon Valley Mountain View, CA rina@microsoft.com Abstract Consider a sequence of bits where we are trying to predict the next bit from the previous bits. ... | 2011 | 24 |
4,302 | Sparse recovery by thresholded non-negative least squares Martin Slawski and Matthias Hein Department of Computer Science Saarland University Campus E 1.1, Saarbr¨ucken, Germany {ms,hein}@cs.uni-saarland.de Abstract Non-negative data are commonly encountered in numerous fields, making nonnegative least s... | 2011 | 240 |
4,303 | A Two-Stage Weighting Framework for Multi-Source Domain Adaptation Qian Sun∗, Rita Chattopadhyay∗, Sethuraman Panchanathan, Jieping Ye Computer Science and Engineering, Arizona State University, AZ 85287 {Qian Sun, rchattop, panch, Jieping.Ye}@asu.edu Abstract Discriminative learning when training and test ... | 2011 | 241 |
4,304 | Variance Reduction in Monte-Carlo Tree Search Joel Veness University of Alberta veness@cs.ualberta.ca Marc Lanctot University of Alberta lanctot@cs.ualberta.ca Michael Bowling University of Alberta bowling@cs.ualberta.ca Abstract Monte-Carlo Tree Search (MCTS) has proven to be a powerful, generic ... | 2011 | 242 |
4,305 | Advice Refinement in Knowledge-Based SVMs Gautam Kunapuli Univ. of Wisconsin-Madison 1300 University Avenue Madison, WI 53705 kunapuli@wisc.edu Richard Maclin Univ. of Minnesota, Duluth 1114 Kirby Drive Duluth, MN 55812 rmaclin@d.umn.edu Jude W. Shavlik Univ. of Wisconsin-Madison 1300 Universit... | 2011 | 243 |
4,306 | On fast approximate submodular minimization Stefanie Jegelka†, Hui Lin∗, Jeff Bilmes∗ † Max Planck Institute for Intelligent Systems, Tuebingen, Germany ∗University of Washington, Dept. of EE, Seattle, U.S.A. jegelka@tuebingen.mgp.de,{hlin,bilmes}@ee.washington.edu Abstract We are motivated by an applicatio... | 2011 | 244 |
4,307 | Multiple Instance Filtering Kamil Wnuk Stefano Soatto University of California, Los Angeles {kwnuk,soatto}@cs.ucla.edu Abstract We propose a robust filtering approach based on semi-supervised and multiple instance learning (MIL). We assume that the posterior density would be unimodal if not for the effect o... | 2011 | 245 |
4,308 | A reinterpretation of the policy oscillation phenomenon in approximate policy iteration Paul Wagner Department of Information and Computer Science Aalto University School of Science PO Box 15400, FI-00076 Aalto, Finland pwagner@cis.hut.fi Abstract A majority of approximate dynamic programming approaches... | 2011 | 246 |
4,309 | θ-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding Congcong Li, Ashutosh Saxena, Tsuhan Chen Cornell University, Ithaca, NY 14853, United States cl758@cornell.edu, asaxena@cs.cornell.edu, tsuhan@ece.cornell.edu Abstract For most scene understanding tasks (such as o... | 2011 | 247 |
4,310 | Maximum Covariance Unfolding: Manifold Learning for Bimodal Data Vijay Mahadevan Department of ECE University of California, San Diego La Jolla, CA 92093 vmahadev@ucsd.edu Chi Wah Wong Department of Radiology University of California, San Diego La Jolla, CA 92093 cwwong@ieee.org Jose Costa Perei... | 2011 | 248 |
4,311 | Noise Thresholds for Spectral Clustering Sivaraman Balakrishnan Min Xu Akshay Krishnamurthy Aarti Singh School of Computer Science, Carnegie Mellon University {sbalakri,minx,akshaykr,aarti}@cs.cmu.edu Abstract Although spectral clustering has enjoyed considerable empirical success in machine learning, i... | 2011 | 249 |
4,312 | Maximum Margin Multi-Instance Learning Hua Wang Computer Science and Engineering University of Texas at Arlington huawangcs@gmail.com Heng Huang Computer Science and Engineering University of Texas at Arlington heng@uta.edu Farhad Kamangar Computer Science and Engineering University of Texas at Ar... | 2011 | 25 |
4,313 | Kernel Embeddings of Latent Tree Graphical Models Le Song College of Computing Georgia Institute of Technology lsong@cc.gatech.edu Ankur P. Parikh School of Computer Science Carnegie Mellon University apparikh@cs.cmu.edu Eric P. Xing School of Computer Science Carnegie Mellon University epxing@c... | 2011 | 250 |
4,314 | Learning a Distance Metric from a Network Blake Shaw∗ Computer Science Dept. Columbia University blake@cs.columbia.edu Bert Huang∗ Computer Science Dept. Columbia University bert@cs.columbia.edu Tony Jebara Computer Science Dept. Columbia University jebara@cs.columbia.edu Abstract Many real-... | 2011 | 251 |
4,315 | Selective Prediction of Financial Trends with Hidden Markov Models Ran El-Yaniv and Dmitry Pidan Department of Computer Science, Technion Haifa, 32000 Israel {rani,pidan}@cs.technion.ac.il Abstract Focusing on short term trend prediction in a financial context, we consider the problem of selective predic... | 2011 | 252 |
4,316 | Blending Autonomous Exploration and Apprenticeship Learning Thomas J. Walsh Center for Educational Testing and Evaluation University of Kansas Lawrence, KS 66045 twalsh@ku.edu Daniel Hewlett Clayton T. Morrison School of Information: Science, Technology and Arts University of Arizona Tucson, A... | 2011 | 253 |
4,317 | Transfer Learning by Borrowing Examples for Multiclass Object Detection Joseph J. Lim CSAIL, MIT lim@csail.mit.edu Ruslan Salakhutdinov Department of Statistics, University of Toronto rsalakhu@utstat.toronto.edu Antonio Torralba CSAIL, MIT torralba@csail.mit.edu Abstract Despite the recent trend... | 2011 | 254 |
4,318 | A blind deconvolution method for neural spike identification Chaitanya Ekanadham Courant Institute New York University New York, NY 10012 chaitu@math.nyu.edu Daniel Tranchina Courant Institute New York University New York, NY 10012 Eero P. Simoncelli Courant Institute Center for Neural Science ... | 2011 | 255 |
4,319 | Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery Scott Niekum Andrew G. Barto Department of Computer Science University of Massachusetts Amherst Amherst, MA 01003 {sniekum,barto}@cs.umass.edu Abstract Skill discovery algorithms in reinforcement learning typically identify s... | 2011 | 256 |
4,320 | Unsupervised learning models of primary cortical receptive fields and receptive field plasticity Andrew Saxe, Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Y. Ng Department of Computer Science Stanford University {asaxe, mbhand, rmudur, bipins, ang}@cs.stanford.edu Abstract The efficient coding hypothesi... | 2011 | 257 |
4,321 | Improved Algorithms for Linear Stochastic Bandits Yasin Abbasi-Yadkori abbasiya@ualberta.ca Dept. of Computing Science University of Alberta D´avid P´al dpal@google.com Dept. of Computing Science University of Alberta Csaba Szepesv´ari szepesva@ualberta.ca Dept. of Computing Science University o... | 2011 | 258 |
4,322 | From Bandits to Experts: On the Value of Side-Observations Shie Mannor Department of Electrical Engineering Technion, Israel shie@ee.technion.ac.il Ohad Shamir Microsoft Research New England USA ohadsh@microsoft.com Abstract We consider an adversarial online learning setting where a decision maker... | 2011 | 259 |
4,323 | Anatomically Constrained Decoding of Finger Flexion from Electrocorticographic Signals Zuoguan Wang Department of ECSE Rensselaer Polytechnic Inst. Troy, NY 12180 wangz6@rpi.edu Gerwin Schalk Wadsworth Center NYS Dept of Health Albany, NY, 12201 schalk@wadsworth.org Qiang Ji Department of ECSE... | 2011 | 26 |
4,324 | Optimal Reinforcement Learning for Gaussian Systems Philipp Hennig Max Planck Institute for Intelligent Systems Department of Empirical Inference Spemannstraße 38, 72070 T¨ubingen, Germany phennig@tuebingen.mpg.de Abstract The exploration-exploitation trade-off is among the central challenges of reinfor... | 2011 | 260 |
4,325 | An Empirical Evaluation of Thompson Sampling Olivier Chapelle Yahoo! Research Santa Clara, CA chap@yahoo-inc.com Lihong Li Yahoo! Research Santa Clara, CA lihong@yahoo-inc.com Abstract Thompson sampling is one of oldest heuristic to address the exploration / exploitation trade-off, but it is surpris... | 2011 | 261 |
4,326 | Efficient Offline Communication Policies for Factored Multiagent POMDPs Jo˜ao V. Messias Institute for Systems and Robotics Instituto Superior T´ecnico Lisbon, Portugal jmessias@isr.ist.utl.pt Matthijs T.J. Spaan Delft University of Technology Delft, The Netherlands m.t.j.spaan@tudelft.nl Pedro U. L... | 2011 | 262 |
4,327 | Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model Learning Xinggang Wang1∗ Xiang Bai1 Xingwei Yang2† Wenyu Liu1 Longin Jan Latecki3 1 Dept. of Electronics and Information Engineering, Huazhong Univ. of Science and Technology, China 2 Image Analytics Lab, GE Resear... | 2011 | 263 |
4,328 | Quasi-Newton Methods for Markov Chain Monte Carlo Yichuan Zhang and Charles Sutton School of Informatics University of Edinburgh Y.Zhang-60@sms.ed.ac.uk, csutton@inf.ed.ac.uk Abstract The performance of Markov chain Monte Carlo methods is often sensitive to the scaling and correlations between the rando... | 2011 | 264 |
4,329 | Inference in continuous-time change-point models Florian Stimberg Computer Science, TU Berlin flostim@cs.tu-berlin.de Andreas Ruttor Computer Science, TU Berlin ruttor@cs.tu-berlin.de Manfred Opper Computer Science, TU Berlin opperm@cs.tu-berlin.de Guido Sanguinetti School of Informatics, Universi... | 2011 | 265 |
4,330 | Universal low-rank matrix recovery from Pauli measurements Yi-Kai Liu Applied and Computational Mathematics Division National Institute of Standards and Technology Gaithersburg, MD, USA yi-kai.liu@nist.gov Abstract We study the problem of reconstructing an unknown matrix M of rank r and dimension d usin... | 2011 | 266 |
4,331 | A Convergence Analysis of Log-Linear Training Simon Wiesler Computer Science Department RWTH Aachen University 52056 Aachen, Germany wiesler@cs.rwth-aachen.de Hermann Ney Computer Science Department RWTH Aachen University 52056 Aachen, Germany ney@cs.rwth-aachen.de Abstract Log-linear models are... | 2011 | 267 |
4,332 | On the accuracy of ℓ1-filtering of signals with block-sparse structure Anatoli Juditsky∗ Fatma Kılınc¸ Karzan† Arkadi Nemirovski‡ Boris Polyak§ Abstract We discuss new methods for the recovery of signals with block-sparse structure, based on ℓ1-minimization. Our emphasis is on the efficiently computable e... | 2011 | 268 |
4,333 | Group Anomaly Detection using Flexible Genre Models Liang Xiong Machine Learning Department, Carnegie Mellon University lxiong@cs.cmu.edu Barnab´as P´oczos Robotics Institute, Carnegie Mellon University bapoczos@cs.cmu.edu Jeff Schneider Robotics Institute, Carnegie Mellon University schneide@cs... | 2011 | 269 |
4,334 | Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons Yan Karklin and Eero P. Simoncelli Howard Hughes Medical Institute and Center for Neural Science New York University New York, NY 10003 {yan.karklin, eero.simoncelli}@nyu.edu Abstract Efficient coding provides a power... | 2011 | 27 |
4,335 | Randomized Algorithms for Comparison-based Search Dominique Tschopp AWK Group Bern, Switzerland dominique.tschopp@gmail.com Suhas Diggavi University of California Los Angeles (UCLA) Los Angeles, CA 90095 suhasdiggavi@ucla.edu Payam Delgosha Sharif University of Technology Tehran, Iran pdelgosh... | 2011 | 270 |
4,336 | Multiple Instance Learning on Structured Data 1Dan Zhang, 2Yan Liu, 1Luo Si, 3Jian Zhang, 4Richard D. Lawrence 1. Computer Science Department, Purdue University, West Lafayette, IN 47906 2. Computer Science Department, University of Southern California, Los Angeles, CA 90089 3. Statistics Department, Purdue Uni... | 2011 | 271 |
4,337 | Probabilistic Joint Image Segmentation and Labeling∗ Adrian Ion1,2, Joao Carreira1, Cristian Sminchisescu1 1Faculty of Mathematics and Natural Sciences, University of Bonn 2 PRIP, Vienna University of Technology & Institute of Science and Technology, Austria {ion,carreira,cristian.sminchisescu}@ins.uni-bonn.de ... | 2011 | 272 |
4,338 | Learning to Search Efficiently in High Dimensions Zhen Li ∗ UIUC zhenli3@uiuc.edu Huazhong Ning Google Inc. huazhong@gooogle.com Liangliang Cao IBM T.J. Watson Research Center liangliang.cao@us.ibm.com Tong Zhang Rutgers University tzhang@stat.rutgers.edu Yihong Gong NEC China ygongca@gmail... | 2011 | 273 |
4,339 | Learning a Tree of Metrics with Disjoint Visual Features Sung Ju Hwang University of Texas Austin, TX 78701 sjhwang@cs.utexas.edu Kristen Grauman University of Texas Austin, TX 78701 grauman@cs.utexas.edu Fei Sha University of Southern California Los Angeles, CA 90089 feisha@usc.edu Abstract... | 2011 | 274 |
4,340 | Monte Carlo Value Iteration with Macro-Actions Zhan Wei Lim David Hsu Wee Sun Lee Department of Computer Science, National University of Singapore Singapore, 117417, Singapore Abstract POMDP planning faces two major computational challenges: large state spaces and long planning horizons. The recently in... | 2011 | 275 |
4,341 | Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels Vikas Sindhwani and Aur´elie C. Lozano IBM T.J. Watson Research Center Yorktown Heights, NY 10598 {vsindhw,aclozano}@us.ibm.com Abstract We consider regularized risk minimization in a large dictionary of Reproducing... | 2011 | 276 |
4,342 | Distributed Delayed Stochastic Optimization Alekh Agarwal John C. Duchi Department of Electrical Engineering and Computer Sciences University of California, Berkeley Berkeley, CA 94720 {alekh,jduchi}@eecs.berkeley.edu Abstract We analyze the convergence of gradient-based optimization algorithms whose ... | 2011 | 277 |
4,343 | An Unsupervised Decontamination Procedure For Improving The Reliability Of Human Judgments Michael C. Mozer,⋆Benjamin Link,⋆Harold Pashler† ⋆Dept. of Computer Science, University of Colorado †Dept. of Psychology, UCSD Abstract Psychologists have long been struck by individuals’ limitations in expressing the... | 2011 | 278 |
4,344 | Contextual Gaussian Process Bandit Optimization Andreas Krause Cheng Soon Ong Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland krausea@ethz.ch chengsoon.ong@inf.ethz.ch Abstract How should we design experiments to maximize performance of a complex system, taking into account uncont... | 2011 | 279 |
4,345 | Multilinear Subspace Regression: An Orthogonal Tensor Decomposition Approach Qibin Zhao 1, Cesar F. Caiafa 2, Danilo P. Mandic 3, Liqing Zhang4, Tonio Ball 5, Andreas Schulze-Bonhage5, and Andrzej Cichocki1 1Brain Science Institute, RIKEN, Japan 2Instituto Argentino de Radioastronom´ıa (IAR), CONICET, Argenti... | 2011 | 28 |
4,346 | Learning with the Weighted Trace-norm under Arbitrary Sampling Distributions Rina Foygel Department of Statistics University of Chicago rina@uchicago.edu Ruslan Salakhutdinov Department of Statistics University of Toronto rsalakhu@ustat.toronto.edu Ohad Shamir Microsoft Research New England ohad... | 2011 | 280 |
4,347 | Demixed Principal Component Analysis Wieland Brendel Ecole Normale Supérieure, Paris, France Champalimaud Neuroscience Programme Lisbon, Portugal Ranulfo Romo Instituto de Fisiología Celular Universidad Nacional Autónoma de México Mexico City, Mexico Christian K. Machens Ecole Normale Supérieure, Pa... | 2011 | 281 |
4,348 | Reconstructing Patterns of Information Diffusion from Incomplete Observations ∗ Flavio Chierichetti Department of Computer Science Cornell University Ithaca, NY 14853 Jon Kleinberg Department of Computer Science Cornell University Ithaca, NY 14853 David Liben-Nowell Department of Computer Science ... | 2011 | 282 |
4,349 | Differentially Private M-Estimators Lei, Jing Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213 jinglei@andrew.cmu.edu Abstract This paper studies privacy preserving M-estimators using perturbed histograms. The proposed approach allows the release of a wide class of M-estimators w... | 2011 | 283 |
4,350 | Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification Ichiro Takeuchi Department of Engineering Nagoya Institute of Technology takeuchi.ichiro@nitech.ac.jp Masashi Sugiyama Department of Computer Science Tokyo Institute of Technology sugi@cs.titech.ac.jp Abstract We consi... | 2011 | 284 |
4,351 | Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms Liefeng Bo University of Washington Seattle WA 98195, USA Xiaofeng Ren ISTC-Pervasive Computing Intel Labs Seattle WA 98195, USA Dieter Fox University of Washington Seattle WA 98195, USA Abstract Extracting g... | 2011 | 285 |
4,352 | Solving Decision Problems with Limited Information Denis D. Mau´a IDSIA Manno, CH 6928 denis@idsia.ch Cassio P. de Campos IDSIA Manno, CH 6928 cassio@idsia.ch Abstract We present a new algorithm for exactly solving decision-making problems represented as an influence diagram. We do not require the us... | 2011 | 286 |
4,353 | How Do Humans Teach: On Curriculum Learning and Teaching Dimension Faisal Khan, Xiaojin Zhu, Bilge Mutlu Department of Computer Sciences, University of Wisconsin–Madison Madison, WI, 53706 USA. {faisal, jerryzhu, bilge}@cs.wisc.edu Abstract We study the empirical strategies that humans follow as they te... | 2011 | 287 |
4,354 | A rational model of causal induction with continuous causes Michael D. Pacer Department of Psychology University of California, Berkeley Berkeley, CA 94720 mpacer@berkeley.edu Thomas L. Griffiths Department of Psychology University of California, Berkeley Berkeley, CA 94720 Tom Griffiths@berkeley.e... | 2011 | 288 |
4,355 | 1 Identifying Alzheimer’s Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis Shuai Huang1, Jing Li1, Jieping Ye2,3, Kewei Chen4, Teresa Wu1, Adam Fleisher4, Eric Reiman4 1Industrial Engineering, 2Computer Science and E... | 2011 | 289 |
4,356 | Large-Scale Sparse Principal Component Analysis with Application to Text Data Youwei Zhang Department of Electrical Engineering and Computer Sciences University of California, Berkeley Berkeley, CA 94720 zyw@eecs.berkeley.edu Laurent El Ghaoui Department of Electrical Engineering and Computer Sciences ... | 2011 | 29 |
4,357 | Structured sparse coding via lateral inhibition Karol Gregor Janelia Farm, HHMI 19700 Helix Drive Ashburn, VA, 20147 karol.gregor@gmail.com Arthur Szlam The City College of New York Convent Ave and 138th st New York, NY, 10031 aszlam@courant.nyu.edu Yann LeCun New York University 715 Broadway,... | 2011 | 290 |
4,358 | TDγ: Re-evaluating Complex Backups in Temporal Difference Learning George Konidaris∗† MIT CSAIL† Cambridge MA 02139 gdk@csail.mit.edu Scott Niekum∗‡ Philip S. Thomas∗‡ University of Massachusetts Amherst‡ Amherst MA 01003 {sniekum,pthomas}@cs.umass.edu Abstract We show that the λ-return target u... | 2011 | 291 |
4,359 | Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron Ryota Kobayashi∗ Department of Human and Computer Intelligence, Ritsumeikan University Siga 525-8577, Japan kobayashi@cns.ci.ritsumei.ac.jp Yasuhiro Tsubo Laboratory for Neural Circuit Theory, Brain Scienc... | 2011 | 292 |
4,360 | Joint 3D Estimation of Objects and Scene Layout Andreas Geiger Karlsruhe Institute of Technology geiger@kit.edu Christian Wojek MPI Saarbr¨ucken cwojek@mpi-inf.mpg.de Raquel Urtasun TTI Chicago rurtasun@ttic.edu Abstract We propose a novel generative model that is able to reason jointly about the ... | 2011 | 293 |
4,361 | Sparse Manifold Clustering and Embedding Ehsan Elhamifar Center for Imaging Science Johns Hopkins University ehsan@cis.jhu.edu Ren´e Vidal Center for Imaging Science Johns Hopkins University rvidal@cis.jhu.edu Abstract We propose an algorithm called Sparse Manifold Clustering and Embedding (SMCE) ... | 2011 | 294 |
4,362 | Submodular Multi-Label Learning James Petterson NICTA/ANU Canberra, Australia Tiberio Caetano NICTA/ANU Sydney/Canberra, Australia Abstract In this paper we present an algorithm to learn a multi-label classifier which attempts at directly optimising the F-score. The key novelty of our formulation is th... | 2011 | 295 |
4,363 | Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities Angela Yao∗ ETH Zurich Juergen Gall ETH Zurich Luc Van Gool ETH Zurich Raquel Urtasun TTI Chicago {yaoa, gall, vangool}@vision.ee.ethz.ch, rurtasun@ttic.edu Abstract A common approach for handling the comple... | 2011 | 296 |
4,364 | Collective Graphical Models Daniel Sheldon Oregon State University sheldon@eecs.oregonstate.edu Thomas G. Dietterich Oregon State University tgd@eecs.oregonstate.edu Abstract There are many settings in which we wish to fit a model of the behavior of individuals but where our data consist only of aggregat... | 2011 | 297 |
4,365 | Sparse Estimation with Structured Dictionaries David P. Wipf ∗ Visual Computing Group Microsoft Research Asia davidwipf@gmail.com Abstract In the vast majority of recent work on sparse estimation algorithms, performance has been evaluated using ideal or quasi-ideal dictionaries (e.g., random Gaussian or... | 2011 | 298 |
4,366 | NEWTRON: an Efficient Bandit algorithm for Online Multiclass Prediction Elad Hazan Department of Industrial Engineering Technion - Israel Institute of Technology Haifa 32000 Israel ehazan@ie.technion.ac.il Satyen Kale Yahoo! Research 4301 Great America Parkway Santa Clara, CA 95054 skale@yahoo-inc.... | 2011 | 299 |
4,367 | Learning Higher-Order Graph Structure with Features by Structure Penalty Shilin Ding1∗, Grace Wahba1,2,3∗, and Xiaojin Zhu2∗ Department of {1Statistics, 2Computer Sciences, 3Biostatistics and Medical Informatics} University of Wisconsin-Madison, WI 53705 {sding, wahba}@stat.wisc.edu, jerryzhu@cs.wisc.edu Ab... | 2011 | 3 |
4,368 | Nearest Neighbor based Greedy Coordinate Descent Inderjit S. Dhillon Department of Computer Science University of Texas at Austin inderjit@cs.utexas.edu Pradeep Raviknmar Department of Computer Science University of Texas at Austin pradeepr@cs.utexas.edu Ambuj Tewari Department of Computer... | 2011 | 30 |
4,369 | The Fixed Points of Off-Policy TD J. Zico Kolter Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 kolter@csail.mit.edu Abstract Off-policy learning, the ability for an agent to learn about a policy other than the one it is following, is a k... | 2011 | 300 |
4,370 | Transfer from Multiple MDPs Alessandro Lazaric INRIA Lille - Nord Europe, Team SequeL, France alessandro.lazaric@inria.fr Marcello Restelli Department of Electronics and Informatics, Politecnico di Milano, Italy restelli@elet.polimi.it Abstract Transfer reinforcement learning (RL) methods leverage on th... | 2011 | 301 |
4,371 | A Pylon Model for Semantic Segmentation Victor Lempitsky Andrea Vedaldi Andrew Zisserman Visual Geometry Group, University of Oxford∗ {vilem,vedaldi,az}@robots.ox.ac.uk Abstract Graph cut optimization is one of the standard workhorses of image segmentation since for binary random field representations of... | 2011 | 302 |
4,372 | How biased are maximum entropy models? Jakob H. Macke Gatsby Computational Neuroscience Unit University College London, UK jakob@gatsby.ucl.ac.uk Iain Murray School of Informatics University of Edinburgh, UK i.murray@ed.ac.uk Peter E. Latham Gatsby Computational Neuroscience Unit University Colleg... | 2011 | 303 |
4,373 | Gaussian process modulated renewal processes Vinayak Rao Gatsby Computational Neuroscience Unit University College London vrao@gatsby.ucl.ac.uk Yee Whye Teh Gatsby Computational Neuroscience Unit University College London ywteh@gatsby.ucl.ac.uk Abstract Renewal processes are generalizations of the P... | 2011 | 304 |
4,374 | An ideal observer model for identifying the reference frame of objects Joseph L. Austerweil Department of Psychology University of California, Berkeley Berkeley, CA 94720 Joseph.Austerweil@gmail.com Abram L. Friesen Department of Computer Science and Engineering University of Washington Seattle, WA ... | 2011 | 305 |
4,375 | Greedy Algorithms for Structurally Constrained High Dimensional Problems Ambuj Tewarl Department of Computer Science University of Texas at Austin ambuj@cs.utexas.edu Pradeep Ravikumar Department of Computer Science University of Texas at Austin pradeepr@cs.utexas.edu Inderjit S. Dhillon ... | 2011 | 306 |
4,376 | Convergent Bounds on the Euclidean Distance Yoonho Hwang Hee-Kap Ahn Department of Computer Science and Engineering Pohang University of Science and Technology POSTECH, Pohang, Gyungbuk, Korea(ROK) {cypher,heekap}@postech.ac.kr Abstract Given a set V of n vectors in d-dimensional space, we provide an ef... | 2011 | 31 |
4,377 | 1 INTRODUCTION 1 Video Annotation and Tracking with Active Learning Carl Vondrick UC Irvine vondrick@mit.edu Deva Ramanan UC Irvine dramanan@ics.uci.edu Abstract We introduce a novel active learning framework for video annotation. By judiciously choosing which frames a user should annotate, we can... | 2011 | 32 |
4,378 | PICODES: Learning a Compact Code for Novel-Category Recognition Alessandro Bergamo, Lorenzo Torresani Dartmouth College Hanover, NH, U.S.A. {aleb, lorenzo}@cs.dartmouth.edu Andrew Fitzgibbon Microsoft Research Cambridge, United Kingdom awf@microsoft.com Abstract We introduce PICODES: a very compac... | 2011 | 33 |
4,379 | Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation Zhouchen Lin Visual Computing Group Microsoft Research Asia Risheng Liu Zhixun Su School of Mathematical Sciences Dalian University of Technology Abstract Many machine learning and signal processing problems ca... | 2011 | 34 |
4,380 | Sparse Filtering Jiquan Ngiam, Pang Wei Koh, Zhenghao Chen, Sonia Bhaskar, Andrew Y. Ng Computer Science Department, Stanford University {jngiam,pangwei,zhenghao,sbhaskar,ang}@cs.stanford.edu Abstract Unsupervised feature learning has been shown to be effective at learning representations that perform well on... | 2011 | 35 |
4,381 | Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts Matthias Hein Saarland University, Saarbr¨ucken, Germany hein@cs.uni-saarland.de Simon Setzer Saarland University, Saarbr¨ucken, Germany setzer@mia.uni-saarland.de Abstract Spectral clustering is based on the spectral relaxation of ... | 2011 | 36 |
4,382 | Why The Brain Separates Face Recognition From Object Recognition Joel Z Leibo, Jim Mutch and Tomaso Poggio Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA 02139 jzleibo@mit.edu, jmutch@mit.edu, tp@ai.mit.edu Abstract Many studies have uncovered evidence that... | 2011 | 37 |
4,383 | Analytical Results for the Error in Filtering of Gaussian Processes Alex Susemihl Bernstein Center for Computational Neuroscience Berlin,Technische Universit¨at Berlin alex.susemihl@bccn-berlin.de Ron Meir Department of Eletrical Engineering, Technion, Haifa rmeir@ee.technion.ac.il Manfred Opper Berns... | 2011 | 38 |
4,384 | Active Learning with a Drifting Distribution Liu Yang Machine Learning Department Carnegie Mellon University liuy@cs.cmu.edu Abstract We study the problem of active learning in a stream-based setting, allowing the distribution of the examples to change over time. We prove upper bounds on the number of p... | 2011 | 39 |
4,385 | Efficient Methods for Overlapping Group Lasso Lei Yuan Arizona State University Tempe, AZ, 85287 Lei.Yuan@asu.edu Jun Liu Arizona State University Tempe, AZ, 85287 j.liu@asu.edu Jieping Ye Arizona State University Tempe, AZ, 85287 jieping.ye@asu.edu Abstract The group Lasso is an extension of... | 2011 | 4 |
4,386 | Evaluating the inverse decision-making approach to preference learning Alan Jern Department of Psychology Carnegie Mellon University ajern@cmu.edu Christopher G. Lucas Department of Psychology Carnegie Mellon University cglucas@andrew.cmu.edu Charles Kemp Department of Psychology Carnegie Mellon... | 2011 | 40 |
4,387 | Policy Gradient Coagent Networks Philip S. Thomas Department of Computer Science University of Massachusetts Amherst Amherst, MA 01002 pthomas@cs.umass.edu Abstract We present a novel class of actor-critic algorithms for actors consisting of sets of interacting modules. We present, analyze theoretically... | 2011 | 41 |
4,388 | Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning Joni Pajarinen Aalto University, Department of Information and Computer Science, P.O. Box 15400, FI-00076 Aalto, Finland Joni.Pajarinen@aalto.fi Jaakko Peltonen Aalto University, Department of Information and Computer Science,... | 2011 | 42 |
4,389 | Spectral Methods for Learning Multivariate Latent Tree Structure Animashree Anandkumar UC Irvine a.anandkumar@uci.edu Kamalika Chaudhuri UC San Diego kamalika@cs.ucsd.edu Daniel Hsu Microsoft Research dahsu@microsoft.com Sham M. Kakade Microsoft Research & University of Pennsylvania skakade@... | 2011 | 43 |
4,390 | Query-Aware MCMC Michael Wick Department of Computer Science University of Massachusetts Amherst, MA mwick@cs.umass.edu Andrew McCallum Department of Computer Science University of Massachusetts Amherst, MA mccallum@cs.umass.edu Abstract Traditional approaches to probabilistic inference such as ... | 2011 | 44 |
4,391 | HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent Feng Niu leonn@cs.wisc.edu Benjamin Recht brecht@cs.wisc.edu Christopher R´e chrisre@cs.wisc.edu Stephen J. Wright swright@cs.wisc.edu Computer Sciences Department University of Wisconsin-Madison Madison, WI 53706 Abst... | 2011 | 45 |
4,392 | ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning Quoc V. Le, Alexandre Karpenko, Jiquan Ngiam and Andrew Y. Ng {quocle,akarpenko,jngiam,ang}@cs.stanford.edu Computer Science Department, Stanford University Abstract Independent Components Analysis (ICA) and its variants have been succe... | 2011 | 46 |
4,393 | Sparse Recovery with Brownian Sensing Alexandra Carpentier INRIA Lille Alexandra.carpentier@inria.fr Odalric-Ambrym Maillard INRIA Lille odalricambrym.maillard@gmail.com R´emi Munos INRIA Lille remi.munos@inria.fr Abstract We consider the problem of recovering the parameter α ∈RK of a sparse funct... | 2011 | 47 |
4,394 | Learning Anchor Planes for Classification Ziming Zhang† L’ubor Ladický‡ Philip H.S. Torr† Amir Saffari†§ † Department of Computing, Oxford Brookes University, Wheatley, Oxford, OX33 1HX, U.K. ‡ Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, U.K. § Sony Computer Entert... | 2011 | 48 |
4,395 | Ranking annotators for crowdsourced labeling tasks Vikas C. Raykar Siemens Healthcare, Malvern, PA, USA vikas.raykar@siemens.com Shipeng Yu Siemens Healthcare, Malvern, PA, USA shipeng.yu@siemens.com Abstract With the advent of crowdsourcing services it has become quite cheap and reasonably effective to... | 2011 | 49 |
4,396 | Priors over Recurrent Continuous Time Processes Ardavan Saeedi Alexandre Bouchard-Cˆot´e Department of Statistics University of British Columbia Abstract We introduce the Gamma-Exponential Process (GEP), a prior over a large family of continuous time stochastic processes. A hierarchical version of this prio... | 2011 | 5 |
4,397 | Metric Learning with Multiple Kernels Jun Wang Huyen Do Adam Woznica Alexandros Kalousis AI Lab, Department of Informatics University of Geneva, Switzerland {Jun.Wang, Huyen.Do, Adam.Woznica, Alexandros.Kalousis}@unige.ch Abstract Metric learning has become a very active research field. The most popu... | 2011 | 50 |
4,398 | A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm Julie Dethier∗ Department of Bioengineering Stanford University, CA 94305 jdethier@stanford.edu Paul Nuyujukian Department of Bioengineering School of Medicine Stanford University, CA 94305 paul@npl.stanf... | 2011 | 51 |
4,399 | Understanding the Intrinsic Memorability of Images Phillip Isola MIT phillipi@mit.edu Devi Parikh TTI-Chicago dparikh@ttic.edu Antonio Torralba MIT torralba@mit.edu Aude Oliva MIT oliva@mit.edu Abstract Artists, advertisers, and photographers are routinely presented with the task of creati... | 2011 | 52 |
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